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| | {{see also|Machine learning terms}} |
| ==Introduction== | | ==Introduction== |
| In machine learning, the term "dynamic" can refer to various concepts depending on its context. Generally speaking, this indicates a system's capacity for change or adaptation in response to new information or input. Examples include updating models based on new training data or adapting robot behavior according to environmental changes. | | In [[machine learning]], the term [[dynamic]] can refer to various concepts depending on its context. Generally speaking, this indicates a system's capacity for change or adaptation in response to new information or input. Examples include updating models based on new [[training data]] or adapting robot behavior according to environmental changes. |
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| In machine learning, dynamic is the same as [[online]], which means something is done frequently or continuously. | | In machine learning, dynamic is the same as '''online''', which means something is done frequently or continuously. |
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| ==Dynamic Models== | | ==Types== |
| Dynamic models in machine learning refer to those models that evolve over time based on new input. In contrast, static models are trained on a fixed dataset and remain unchanged once deployed. Dynamic models are commonly employed in applications like time series forecasting, where the goal is to predict future values from past data. To remain effective in these cases, models must have the capacity for updating their predictions as new information becomes available. | | #[[Dynamic model]] ([[online model]]): a model that is [[retrain]]ed continuously or frequently |
| | #[[Dynamic training]] ([[online training]]): training continuously or frequently. |
| | #[[Dynamic inference]] ([[online inference]]): generating predictions on demand. |
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| Dynamic models come in many different forms, but all share the capability of adapting to new input. Recurrent neural network (RNN) is one such dynamic model often employed for time series forecasting. An RNN processes input data sequentially and maintains an internal state that changes at each time step; this allows it to retain knowledge about past inputs while making accurate predictions about future ones.
| | ==Example== |
| | As an [[example]] of dynamic machine learning in action, consider an online recommendation system that suggests products or services to users based on their past behavior. As new data becomes available (like a user's recent search history or purchase history), the system can adjust its recommendations to better suit the individual's current interests and preferences. |
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| Another example of a dynamic model is an online learning algorithm. These programs update their predictions in real-time as new data becomes available, making them ideal for applications where input data is constantly changing, such as online advertising or fraud detection.
| | Dynamic systems in machine learning can also be utilized for [[predictive modeling]], where the model is trained on historical [[data]] and then used to make predictions about future events. As new information becomes available, the model can be updated and retrained with this updated information, leading to increasingly accurate predictions over time. |
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| ==Dynamic Environments==
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| Dynamic models and machine learning can also be employed to construct systems that function in dynamic environments. A dynamic environment refers to an area in which input data or task requirements may alter over time. For instance, a robot navigating such an environment must be able to adjust its behavior in order to avoid obstacles and reach its objective.
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| Machine learning researchers often employ reinforcement learning when creating systems that can operate in dynamic environments. Reinforcement learning involves teaching an agent how to take actions that maximize a reward signal, but in dynamic environments this signal may change over time, necessitating the agent to adjust its policy based on new feedback. | | [[Category:Terms]] [[Category:Machine learning terms]] |
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| ==Dynamic Data==
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| Finally, the term "dynamic" can also refer to the nature of input data itself. In some instances, this input data may be continuously altering or developing; an example is social media analysis applications where information is generated in real-time.
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| Machine learning researchers often utilize techniques such as streaming algorithms or online learning to handle dynamic data. Streaming algorithms allow for real-time processing of new information without needing to store the entire dataset in memory, while online learning algorithms update their predictions continuously with newly available information.
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| ==Explain Like I'm 5 (ELI5)==
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| Dynamic machine learning refers to any process or behavior that is capable of altering over time. This can be seen in various ways, such as when a computer program learns from new information or when robots alter their behavior in order to avoid obstacles. It's like learning a new game or skill; the more practice you put into it, the better at it you become at it. Dynamic machine learning helps computers get better at tasks by updating their understanding as they receive updated data.
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